Entry Points – How to Get Rolling with Big Data Analytics


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The Briefing Room with Robin Bloor and IBM
Live Webcast Sept. 24, 2013
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?AT=pb&SP=EC&rID=7501927&rKey=664935ceb7de1aec

Where to begin? That question remains prominent for many organizations who are trying to leverage the value of big data analytics. Most sources of big data are quite different than traditional enterprise data systems. This requires new skill sets, both for the granular integration work, as well as the strategic business perspective required to design useful solutions.

Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor as he explains the pain points associated with modern data volumes and types. He will be briefed by Rick Clements of IBM, who will tout IBM's big data platform, specifically InfoSphere BigInsights, InfoSphere Streams and InfoSphere Data Explorer. He will also present specific use cases that demonstrate how IT and the line of business can springboard over existing challenges, gain insight and improve operational performance.

Visit InsideAnalysis.com for more information

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Entry Points – How to Get Rolling with Big Data Analytics

  1. 1. Entry Points – How to Get Rolling with Big Data Analytics The Briefing Room
  2. 2. Welcome Host: Eric Kavanagh eric.kavanagh@bloorgroup.com Twitter Tag: #briefr The Briefing Room
  3. 3. Mission !   Reveal the essential characteristics of enterprise software, good and bad !   Provide a forum for detailed analysis of today s innovative technologies !   Give vendors a chance to explain their product to savvy analysts !   Allow audience members to pose serious questions... and get answers! Twitter Tag: #briefr The Briefing Room
  4. 4. Topics This Month: ANALYTICS October: DATA PROCESSING November: DATA DISCOVERY & VISUALIZATION Twitter Tag: #briefr The Briefing Room
  5. 5. Analytics Twitter Tag: #briefr The Briefing Room
  6. 6. Analyst: Robin Bloor Robin Bloor is Chief Analyst at The Bloor Group robin.bloor@bloorgroup.com Twitter Tag: #briefr The Briefing Room
  7. 7. IBM !   IBM offers an enterprise class big data platform with capabilities such as Hadoop-based analytics, stream computing and data warehousing !   The platform includes InfoSphere BigInsights, InfoSphere Streams and InfoSphere Data Explorer !   The portfolio of products combines traditional technologies that are ideal for structured tasks with new technologies that address ad hoc data exploration, discovery and unstructured analysis Twitter Tag: #briefr The Briefing Room
  8. 8. Guests: Rick Clements & Vijay Ramaiah   Rick Clements is Program Director, Worldwide Big Data Product Marketing for IBM. In his current role, he is responsible for global product marketing for the IBM big data platform including positioning and messaging for InfoSphere BigInsights, InfoSphere Streams and InfoSphere Data Explorer. Mr. Clements has 14 years experience in the software industry and deep knowledge and understanding in the areas of enterprise application integration, business to business integration, business process management, service oriented architecture, web services, business activity monitoring, master data management and big data technologies.   Vijay Ramaiah is Worldwide Product Manager, IBM Big Data Portfolio for IBM. He is responsible for driving portfolio strategy for the IBM big data software platform and accelerators, and leading cross-organizational strategy and execution plans. Mr. Ramaiah also manages the portfolio of Big Data Accelerators, which includes Social Data Analytics, Machine Data Analytics and Telco Call Data Analytics. He has 23 years of software business, market and technology experience. Twitter Tag: #briefr The Briefing Room
  9. 9. Richard Clements, Program Director, Big Data Product Marketing Unlocking New Insights and Opportunities with Big Data © 2013 IBM Corporation
  10. 10. Big Data – the 5 Key Use Cases Big Data Exploration Find, visualize, understand all big data to improve decision making Enhanced 360o View of the Customer Security/Intelligence Extension Extend existing customer views by incorporating additional internal and external information sources Lower risk, detect fraud and monitor cyber security in realtime Operations Analysis Data Warehouse Augmentation Analyze a variety of machine data for improved business results Integrate big data and data warehouse capabilities to increase operational efficiency 10 © 2013 IBM Corporation
  11. 11. Enhanced 360º View of the Customer: Needs Optimize every customer interaction by knowing everything about them Requirements Industry Examples Create a connected picture of the customer •  Smart meter analysis Mine all existing and new sources of information Analyze social media to uncover sentiment about products Add value by optimizing every client interaction •  Telco data location monetization •  Retail marketing optimization •  Travel and Transport customer analytics and loyalty marketing •  Financial Services Next Best Action and customer retention •  Automotive warranty claims •  … 11 © 2013 IBM Corporation
  12. 12. A customer is a puzzle made up of many pieces Business Context Contact Information Name, address, employer, marital… Account number, customer type, purchase history, … Every interaction requires someone to piece together Legal/Financial Life parts of the Property, credit rating, vehicles, … puzzle Social Media Social network, affiliations, network … Professional Life Employers, professional groups, certifications … Leisure Hobbies, interests … Information about your customers is dispersed, forcing your employees to extract it pieceby-piece 12 © 2013 IBM Corporation
  13. 13. Analy&cs  based  on   accurate  data  and   contextual  intelligence   Customer  info  from   MDM     Recent  conversa&ons   from  mul&ple  sources:   e.g.,  CRM,  e-­‐mail,  etc.   13 © 2013 IBM Corporation © 2013 IBM Corporation
  14. 14. Data Warehouse Augmentation: Needs Exploit technology advances to deliver more value from an existing data warehouse investment while reducing cost! Requirements Add new sources to existing DW investments Optimize storage & provide query-able archive Rationalize for greater simplicity and lower cost Enable complex analytical applications with faster queries Improve DW performance by determining which data to put into it Scale predictive analytics and business intelligence Leverage variety of data for deep analysis Examples • Pre-Processing Hub • Queryable Data Store • Exploratory Analysis • Operational Reporting • Real-time Scoring • Segmentation and Modeling 14 © 2013 IBM Corporation
  15. 15. 3 Ways to Augment Your Data Warehouse 1 Pre-Processing Hub 2 Queryable Data Store 3 Exploratory Analysis 15 © 2013 IBM Corporation
  16. 16. How some organizations are using this today… Discover and visualize fraud patterns, account closings, activity patterns from data that was once unable to be leveraged Increase the spectrum for data analysis from 30 days to multiple years – allowing for more accurate decision making Reducing costs and increasing the quality of service by offloading colder data onto Hadoop with commodity hardware To glean more information about customers at the individual level by analyzing social media with operational data 16 © 2013 IBM Corporation
  17. 17. Big Data Exploration: Needs Explore and mine big data to find what is interesting and relevant to the business 
 for better decision making! Requirements Industry Examples Explore new data sources for potential value •  Customer service knowledge portal Mine for what is relevant for a business imperative Assess the business value of unstructured content Uncover patterns with visualization and algorithms Prevent exposure of sensitive information •  Insurance catastrophe modeling •  Automotive features and pricing optimization •  Chemicals and Petroleum conditioned base maintenance •  Life Sciences drug effectiveness … 17 © 2013 IBM Corporation
  18. 18. Security Intelligence Extension: Needs Enhance traditional security solutions to predict, prevent and take action against crime by analyzing all types and sources of big data! Requirements Enhanced Intelligence and Surveillance Insight Analyze data-in-motion and at rest to: •  Find associations •  Uncover patterns and facts •  Maintain currency of information Real-time Cyber Attack Prediction and Mitigation Analyze network traffic to: •  Discover new threats sooner •  Detect known complex threats •  Take action in real-time Crime Prediction and Protection Analyze telco and social data to: •  Gather criminal evidence •  Prevent criminal activities •  Proactively apprehend criminals Industry Examples •  Government threat and crime prediction and prevention •  Insurance claims fraud •  Utilities are terror targets, disrupt power and water •  Retailers vulnerable to internal and external threats due to PCI data 18 © 2013 IBM Corporation
  19. 19. Operations Analysis: Needs Apply analytics to machine data for greater operational efficiency ! Requirements Analyze machine data to identify events of interest Apply predictive models to identify potential anomalies Combine information to understand service levels Monitor systems to avoid service degradation or outages Gain real-time visibility into operations, customer experience, transactions and behavior Proactively plan to increase operational efficiency Industry Examples •  Automotive advanced condition monitoring •  Chemical and Petroleum condition-based Maintenance •  Energy and Utility condition-based maintenance •  Telco campaign management •  Travel and Transport real-time predictive maintenance •  … 19 © 2013 IBM Corporation
  20. 20. IBM Provides a Holistic and Integrated Approach to Big Data and Analytics CONSULTING and IMPLEMENTATION SERVICES §  Assemble and combine relevant mix of information SOLUTIONS Sales | Marketing | Finance | Operations | IT | Risk | HR Industry Risk Analytics Decision Management Content Analytics Business Intelligence and Predictive Analytics Hadoop System Stream Computing §  Take action and automate processes §  Optimize analytical performance and IT costs §  Reduced infrastructure complexity and cost BIG DATA PLATFORM Content Management §  Discover and explore with smart visualizations §  Analyze, predict and automate for more accurate answers ANALYTICS Performance Management Enabling organizations to Data Warehouse §  Manage, govern and secure information Information Integration and Governance SECURITY, SYSTEMS, STORAGE AND CLOUD 20 © 2013 IBM Corporation
  21. 21. The Platform for New Insight and Applications InfoSphere Data Explorer BIG DATA PLATFORM Systems Management Application Development Discovery & Navigation InfoSphere BigInsights for Hadoop Accelerators Hadoop System Stream Computing Discover, understand, search, and navigate federated sources of big data Data Warehouse Information Integration & Governance Cost-effectively analyze Petabytes of unstructured and structured data InfoSphere Streams Analyze streaming data and large data bursts for real-time insights Data Media Content Machine Social 21 © 2013 IBM Corporation
  22. 22. New Architecture to Leverage All Data and Analytics Streams Data  in   Mo&on   Information Ingestion and Operational Information §  Stream Processing §  Data Integration §  Master Data Data  at   Rest   Data  in   Many  Forms   Real-time Analytics §  §  §  §  Video/Audio Network/Sensor Entity Analytics Predictive Landing Area, Analytics Zone and Archive §  §  §  §  §  §  Intelligence Analysis Exploration, Integrated Warehouse, and Mart Zones §  §  §  §  Discovery Deep Reflection Operational Predictive Raw Data Structured Data Text Analytics Data Mining Entity Analytics Machine Learning Decision Management BI and Predictive Analytics Navigation and Discovery Information Governance, Security and Business Continuity 22 © 2013 IBM Corporation
  23. 23. Thank you
  24. 24. Perceptions & Questions Analyst: Robin Bloor Twitter Tag: #briefr The Briefing Room
  25. 25. Big Data Means ??? BIG DATA BIG PROCESSING POWER In reality is really MORE DATA: Yes, for sure if it’s useful DATA SCIENCE: Yes, if it’s needed REAL-TIME ANALYSIS: Yes, for sure if it’s useful NEW BUSINESS OPPORTUNITIES: Yes, possibly
  26. 26. A Disturbance in the Force
  27. 27. Disruption by Acceleration We observe the following: Small Scale Parallelism SSD Replacing Large Scale Spinning Disk Parallelism Cloud Deployment At the processor level, possibly including GPUs, FPGAs, etc. Faster I/O Faster external or internal deployments Massively parallel architectures
  28. 28. Where the Rubber Meets the Road In respect of BIG DATA, many of the new applications are improvements on “familiar” applications: u  THE USUAL SUSPECTS – security, fraud, telco churn, banking (trading & risk), etc. u  GRADUATES – Retail, insurance, healthcare, risk management, etc. u  NEW KIDS ON THE BLOCK – mobile apps, social media, gaming, web advertising u  OPPORTUNITY PLAYERS – machines, devices, etc.) smart products (transport,
  29. 29. The Implications The question for most organizations is: How do we exploit the additional power? This is a BUSINESS question, not a TECHNICAL question.
  30. 30. u  Who is the “data explorer,” in IBM’s view? u  Does IBM believe that data streaming (with analysis) is now ready for prime time? u  The customer context has particular interest since it affects most companies. Does IBM see this as mainly an operational (i.e., near-real time) application? u  There seems to be a conflict to resolve between “new Hadoop” and “traditional data warehouse.” What is IBM’s perspective?
  31. 31. u  How is it possible to define and monitor service levels with big data? u  Big data naturally raises issues about data governance. In IBM’s view, does more data mean that governance become more difficult? u  Does IBM view its Watson technology as a component of big data applications?
  32. 32. Twitter Tag: #briefr The Briefing Room
  33. 33. Upcoming Topics September: ANALYTICS October: DATA PROCESSING November: DATA DISCOVERY & VISUALIZATION www.insideanalysis.com Twitter Tag: #briefr The Briefing Room
  34. 34. Thank You for Your Attention Image credits: 1.  Jonathan Zander: http://en.wikipedia.org/wiki/File:MicroATX_Motherboard_with_AMD_Athlon_Processor_2_Digon3.jpg 2.  Nisky.com: http://niskey.com/ssd-drive-the-new-wave/ 3.  Answers.com: http://www.answers.com/topic/massively-parallel Twitter Tag: #briefr The Briefing Room